Computer Science

We consider distributions of scientific journals impact factor. Analysing 9028 scientific journals with the largest impact factors, we found that the distribution of them is year-to-year stable (at least for analysed 2011-2013 years), and it has the character of the exponential Boltzmann distribution with the power law asymptotic (tail).

The current work addresses a theme previously unexplored in the literature: that of whether the results arising from research activity in fields other than the scientist's pri-mary field have greater value than the others. Operationally, the authors proceed by identifying: the scientific production of each researcher under observation; field classifi-cation of the publications; the field containing the greatest number of the researcher's publications; attribution of value of each publication. The results show that diversifica-tion at the aggregate level does not pay off, although there are some exceptions at the level of individual disciplines. The implications at policy level are notable. Since the in-centive systems of research organizations are based on the impact of scientific output, the scientists concerned could resist engaging in multidisciplinary projects.

Power laws are a characteristic distribution that are ubiquitous, in that they are found almost everywhere, in both natural as well as in man-made systems. They tend to emerge in large, connected and self-organizing systems, for example, scholarly publications. Citations to scientific papers have been found to follow a power law, i.e., the number of papers having a certain level of citation x are proportional to x raised to some negative power. The distributional character of altmetrics has not been studied yet as altmetrics are among the newest indicators related to scholarly publications. Here we select a data sample from the altmetrics aggregator this http URL containing records from the platforms Facebook, Twitter, News, Blogs, etc., and the composite variable Alt-score for the period 2016. The individual and the composite data series of 'mentions' on the various platforms are fit to a power law distribution, and the parameters and goodness of fit determined using least squares regression. The log-log plot of the data, 'mentions' vs. number of papers, falls on an approximately linear line, suggesting the plausibility of a power law distribution. The fit is not very good in all cases due to large fluctuations in the tail. We show that fit to the power law can be improved by truncating the data series to eliminate large fluctuations in the tail. We conclude that altmetric distributions also follow power laws with a fairly good fit over a wide range of values. More rigorous methods of determination may not be necessary at present.

Recent years have seen fast growth in the number of policies mandating Open Access (OA) to research outputs. We conduct a large-scale analysis of over 800 thousand papers from repositories around the world published over a period of 5 years to investigate: a) if the time lag between the date of publication and date of deposit in a repository can be effectively tracked across thousands of repositories globally, and b) if introducing deposit deadlines is associated with a reduction of time from acceptance to public availability of research outputs. We show that after the introduction of the UK REF 2021 OA policy, this time lag has decreased significantly in the UK and that the policy introduction might have accelerated the UK's move towards immediate OA compared to other countries. This supports the argument for the inclusion of a time-limited deposit requirement in OA policies.

The effects of Open Access (OA) upon journal performance are investigated. The key research question holds: How does the citation impact and publication output of journals switching ('flipping') from non-OA to Gold-OA develop after their switch to Gold-OA? A review is given of the literature, with an emphasis on studies dealing with flipping journals. Two study sets with 119 and 100 flipping journals, derived from two different OA data sources (DOAJ and OAD), are compared with two control groups, one based on a standard bibliometric criterion, and a second controlling for a journal's national orientation. Comparing post-switch indicators with pre-switch ones in paired T-tests, evidence was obtained of an OA Citation advantage but not of an OA Publication Advantage. Shifts in the affiliation countries of publishing and citing authors are characterized in terms of countries' income class and geographical world region. Suggestions are made for qualitative follow-up studies to obtain more insight into OA flipping or reverse-flipping

In this feasibility study, the impact of academic research from social sciences and humanities on technological innovation is explored through a study of citations patterns of journal articles in patents. Specifically we focus on citations of journals from the field of environmental economics in patents included in an American patent database (USPTO). Three decades of patents have led to a small set of journal articles (85) that are being cited from the field of environmental economics. While this route of measuring how academic research is validated through its role in stimulating technological progress may be rather limited (based on this first exploration), it may still point to a valuable and interesting topic for further research.

One of the main indicators of scientific development of a given country is the number of papers published in high impact scholarly journals. Many countries introduced performance-based research funding systems (PRFSs) to create a more competitive environment where prolific researchers get rewarded with subsidies to increase both the quantity and quality of papers. Yet, subsidies do not always function as a leverage to improve the citation impact of scholarly papers. This paper investigates the effect of the publication support system of Turkey (TR) on the citation impact of papers authored by Turkish researchers. Based on a stratified probabilistic sample of 4,521 TR-addressed papers, it compares the number of citations to determine if supported papers were cited more often than those of not supported ones, and if they were published in journals with relatively higher citation impact in terms of journal impact factors, article influence scores and quartiles. Both supported and not supported papers received comparable number of citations per paper, and were published in journals with similar citation impact values. Findings suggest that subsidies do not seem to be an effective incentive to improve the quality of scholarly papers. Such support programs should therefore be reconsidered.

Social media platforms have now emerged as an important medium for wider dissemination of research articles; with authors, readers and publishers creating different kinds of social media activity about the article. Some research studies have even shown that articles that get more social media attention may get higher visibility and citations. These factors are now persuading journal publishers to integrate social media plugins in their webpages to facilitate sharing and dissemination of articles in social media platforms. Many past studies have analyzed several factors (like journal impact factor, open access, collaboration etc.) that may impact social media attention of scholarly articles. However, there are no studies to analyze whether the presence of social media plugin in a journal could result in higher social media attention of articles published in the journal. This paper aims to bridge this gap in knowledge by analyzing a sufficiently large-sized sample of 99,749 articles from 100 different journals. Results obtained show that journals that have social media plugins integrated in their webpages get significantly higher social media mentions and shares for their articles as compared to journals that do not provide such plugins. Authors and readers visiting journal webpages appear to be a major contributor to social media activity around articles published in such journals. The results suggest that publishing houses should actively provide social media plugin integration in their journal webpages to increase social media visibility (altmetric impact) of their articles.

Recently, Hirsch (2019a) proposed a new variant of the h index called the h α index. He formulated as follows: "we define the h α index of a scientist as the number of papers in the h-core of the scientist (i.e. the set of papers that contribute to the h-index of the scientist) where this scientist is the α -author" (p. 673). The h α index was criticized by Leydesdorff, Bornmann, and Opthof (2019). One of their most important points is that the index reinforces the Matthew effect in science. We address this point in the current study using a recently developed Stata command (h_index) and R package (hindex), which can be used to simulate h index and h α index applications in research evaluation. The user can investigate under which conditions h α reinforces the Matthew effect. The results of our study confirm what Leydesdorff et al. (2019) expected: the h α index reinforces the Matthew effect. This effect can be intensified if strategic behavior of the publishing scientists and cumulative advantage effects are additionally considered in the simulation.

This paper presents a contribution to the study of bibliographic corpora in the context of science mapping. Starting from a graph representation of documents and their textual dimension, we observe that stochastic block models (SBMs) can provide a simultaneous clustering of documents and words that we call a domain-topic model. Previous work by (Gerlach et al., 2018) investigated the resulting topics, or word clusters, while ours focuses on the study of the document clusters, which we call domains. To enable the synthetic description and interactive navigation of domains, we introduce measures and interfaces relating both types of clusters, which reflect the structure of the graph and the model. We then present a procedure that, starting from the document clusters, extends the block model to also cluster arbitrary metadata attributes of the documents. We call this procedure a domain-chained model, and our previous measures and interfaces can be directly transposed to read the metadata clusters. We provide an example application to a corpus that is relevant to current STS research, and an interesting case for our approach: the 1995-2017 collection of abstracts presented at ASCO, the main annual oncology research conference. Through a sequence of domain-topic and domain-chained models, we identify and describe a particular group of domains in ASCO that have notably grown through the last decades, and which we relate to the establishment of "oncopolicy" as a major concern in oncology.